The stratified mammalian epidermis develops from somatic ectoderm by the progressive addition and differentiation of superficial epidermal layers (1). After the initial morphogenesis, the epidermis is maintained by homeostatic mechanisms whereby keratinocytes move from the proliferating basal cell layer towards the surface as they undergo a series of differentiation steps, ultimately undergoing cell death. One role of keratinocyte differentiation is the formation of an effective permeability barrier, which depends on the regulated expression of a large battery of genes encoding components of the cornified and granular layers (2). Under funding by the parent grant, we are using molecular biology to investigate the role of a single transcription factor, the Grainyhead-like transcription factor Get1/Grhl3 (3-5), in epidermal differentiation. In contrast, in this revision application, we propose a systems biology approach to computationally generate transcriptional regulatory network for the process of epidermal differentiation. Thus, this application leads to a new direction and differs from the parent grant in two important ways: First, it expands the scope of the study to transcriptional regulatory networks in epidermal development in general, and second, it incorporates the methods of systems biology;thus the application is consistent with one of the key emphasis areas of this RFA (Developmental Biology - Systems biology). The specific goals are to: 1) Generate global gene expression data over the time course of epidermal keratinocyte differentiation;and 2) Use the time course gene expression data from 1, other genome-wide expression data, DNA motif information, functional annotation, and literature mining to infer transcriptional networks in epidermal differentiation. The goal of this project is also to build a new interdisciplinary team between the Andersen laboratory, whose primary expertise is in molecular/developmental biology, and Alex Ihler, a new assistant professor whose primary expertise is in graphical models, Bayesian inference methods, machine learning, and data mining. The contributions of the Ihler group -- the mathematical modeling required to define the networks -- will allow approaches and advances in our understanding of epidermal differentiation that would not be attainable without this collaboration.